Data Engineer
Step-by-Step Career Roadmap Guide to Get Job-Ready
Data engineering is one of tech’s most lucrative careers, powering the systems behind AI, analytics, and real-time products. As cloud and data demand grow, data engineers continue to earn strong salaries and stay highly valuable through 2030.
Data engineering is one of tech’s most lucrative careers, powering the systems behind AI, analytics, and real-time produ...
213,000+
$136,015
Top Industries
Hiring Data Engineer
80%
Job Satisfaction
What Does a Data Engineer Do and Why Businesses Need Them?
Data engineers build the systems that move, store, and prepare data for analytics, reporting, and AI. They create pipelines, maintain data platforms, improve data quality, and ensure teams can access reliable data efficiently and at scale.
Data engineers build the systems that move, store, and prepare data for analytics, reporting, and AI. They create pipelines, maintain data platforms, improve data quality, and ensure teams can access reliable data efficiently and at scale.
Pipeline Design and ETL
Build scalable batch and streaming data pipelines
Data Modeling and Warehousing
Design schemas for efficient analytics and storage
Data Quality and Observability
Monitor freshness, accuracy, and pipeline reliability
Platform and Infrastructure
Manage data platforms, orchestration, & compute systems
Who Is This Career For?
The data engineer role is a good fit for those who are:
Systems and Infrastructure Minded
Interested in building scalable pipelines and improving data flow across systems
Analytical and Quality Focused
Comfortable with data quality, schema design, reliability, and reporting accuracy
Technically Strong and Platform-Oriented
Drawn to databases, cloud tools, orchestration, and systems that keep data usable at scale
Recommended Courses
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Data Engineer Salary Snapshot
Earning potential rises as data engineers move into platform and architecture ownership.
$98,702 – $147,562
+8% Annually
Associate Data Engineer
$103,578 – $170,572
+13% Annually
Data Engineer
$138,345 – $224,666
+17% Annually
Lead Data Engineer
Associate Data Engineer
$98,702 – $147,562
Data Engineer
$103,578 – $170,572
Lead Data Engineer
$138,345 – $224,666
*All salary figures referenced are based on data reported by employees on Glassdoor.
Step-by-Step Data Engineer Roadmap
A comprehensive guide to skills, responsibilities, and expectations at each career level
Who This Is For
Early-career professionals entering data engineering
Candidates moving from adjacent technical roles
Those exploring ETL or data platform paths
Early-career professionals entering data engineering
Candidates moving from adjacent technical roles
Those exploring ETL or data platform paths
Role Outcomes
Build and run batch ETL jobs
Write SQL for data transformation
Support pipeline monitoring and alerting
Deliver clean data to analysts and dashboards
Tool Stack
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Technical Skills
SQL Fundamentals
Python Scripting
ETL Concepts
Basic Data Modeling
Data Warehouse Basics
SQL Fundamentals
Python Scripting
ETL Concepts
Basic Data Modeling
Data Warehouse Basics
+ 4 more skills
Soft Skills
Structured Thinking
Written Documentation
Stakeholder Management
Attention to Data Correctness
Structured Thinking
Written Documentation
Stakeholder Management
Attention to Data Correctness
Example Deliverables
ETL Job Documentation
Document job logic, source systems, transformations, schedules, and dependencies.
Data Quality Check Script
Validate missing values, duplicates, schema issues, and business rule mismatches.
Source-to-Target Mapping
Map source fields to target tables with transformation rules and data types.
KPIs
Pipeline Success Rate
Data Freshness SLA
Row Count Validation Pass Rate
Job Run Duration
Bug Fix Turnaround Time
Interview Checkpoint
A daily pipeline has been running for two months and suddenly fails to load rows. Walk me through how you would debug it.
How would you design a simple ETL pipeline that ingests data from a REST API and loads it into a data warehouse?
What does a good data quality check look like, and at what point in a pipeline would you apply it?
Early-career professionals entering data engineering
Candidates moving from adjacent technical roles
Those exploring ETL or data platform paths
Early-career professionals entering data engineering
Candidates moving from adjacent technical roles
Those exploring ETL or data platform paths
Build and run batch ETL jobs
Write SQL for data transformation
Support pipeline monitoring and alerting
Deliver clean data to analysts and dashboards
SQL Fundamentals
Python Scripting
ETL Concepts
Basic Data Modeling
Data Warehouse Basics
SQL Fundamentals
Python Scripting
ETL Concepts
Basic Data Modeling
Data Warehouse Basics
+ 4 more skills
Structured Thinking
Written Documentation
Stakeholder Management
Attention to Data Correctness
Structured Thinking
Written Documentation
Stakeholder Management
Attention to Data Correctness
ETL Job Documentation
Document job logic, source systems, transformations, schedules, and dependencies.
Data Quality Check Script
Validate missing values, duplicates, schema issues, and business rule mismatches.
Source-to-Target Mapping
Map source fields to target tables with transformation rules and data types.
Pipeline Success Rate
Data Freshness SLA
Row Count Validation Pass Rate
Job Run Duration
Bug Fix Turnaround Time
A daily pipeline has been running for two months and suddenly fails to load rows. Walk me through how you would debug it.
How would you design a simple ETL pipeline that ingests data from a REST API and loads it into a data warehouse?
What does a good data quality check look like, and at what point in a pipeline would you apply it?
Key Things to Know
Your first role typically focuses on learning the team's workflows, running and monitoring existing pipelines, writing SQL for transformation jobs, and gradually taking independent ownership of small pieces of the data stack.
Strong SQL, basic Python, comfort with cloud storage concepts, attention to detail around data correctness, and the ability to document your work clearly are the most important starting skills.
They often own a pipeline domain, warehouse layer, or platform component, along with the quality, stability, and performance of that area.
The focus shifts from executing pipelines to setting the platform direction, making architectural trade-offs, and guiding multiple teams toward shared data infrastructure goals.
Success is usually tied to platform reliability, infrastructure cost efficiency, team velocity, and how effectively you help engineering and product teams access and use data with confidence.
How to Get Started
Your learning roadmap from a complete beginner to a job-ready data engineer
1. Data Engineering Foundations
Learn
Role clarity across core data roles
Pipelines, ETL, warehouses, and data lakes
Schemas, orchestration, and data quality
Cloud data flow fundamentals
Practice & Deliver
1 SQL Query Set on a Sample Dataset
1 Basic Python Script for Data Ingestion
1 Data Model Sketch for a Fictional Business Use Case
Pick A Learning Path
Track A
- SQL fundamentals
- Python basics
- Data warehouse orientation
Track B
- Data concepts overview
- Cloud storage basics
- Pipeline literacy
Track C
- Program orientation
- Intro to data engineering
- SQL and Python foundation
2. Core Pipeline and Modeling Skills
Learn
ETL patterns and batch pipeline fundamentals
Data modeling and warehouse design basics
dbt, cloud storage, and compute concepts
SQL for data transformation
Practice & Deliver
1 End-To-End Batch Pipeline Project
1 Data Model with Documented Business Logic
1 dbt Project with Tests and Documentation
Pick A Learning Path
Track A
- SQL for data engineering
- dbt basics
- Cloud warehouse setup
Track B
- Python ETL scripting
- Pipeline orchestration with Airflow
- Data quality checks
Track C
- Guided pipeline labs
- Ingestion, transformation, and loading modules
3. Cloud Platforms and Orchestration
Learn
Cloud data platform administration basics
Airflow DAG design and scheduling patterns
Pipeline monitoring, alerting, and SLA management
Practice & Deliver
1 Airflow DAG for a Scheduled Pipeline
1 Cloud Data Warehouse Project with Documented Design
1 Pipeline Monitoring Dashboard
Pick A Learning Path
Track A
- Cloud platform deep dive
- Orchestration basics
Track B
- Airflow advanced patterns
- Pipeline monitoring and alerting
Track C
- Guided capstone project
- Mentor review
4. Projects and Portfolio
Learn
Build case studies around pipeline design decisions and architecture choices
Present options considered and tradeoffs made
Explain why you chose your approach and what you would do differently
Highlight measurable outcomes such as SLA improvement, cost reduction, or reliability gains
Practice & Deliver
End-To-End batch Pipeline Project
Streaming Ingestion Prototype
Data Model Redesign Case Study
Data Quality Framework Implementation
Cloud Cost Optimization Analysis
Pick A Learning Path
Track A
- 2 Pipeline case studies
- 1 Data model write-up
Track B
- 1 Coud architecture case study
- 1 Real-time ingestion project
- 1 Data quality framework build
Track C
- Capstone Project
- Portfolio refinement and review
5. Choose Your Specialization
Learn
Streaming and real-time engineering: Kafka, Flink, Kinesis, and event-driven pipeline patterns
Lakehouse and platform engineering: Delta Lake, Apache Iceberg, Databricks, and Medallion architecture
Analytics engineering: dbt advanced, Data modeling standard
ML infrastructure: Feature engineering, Data pipelines for ML, and DataOps practices
Practice & Deliver
1 Specialization-Aligned Project
1 Architecture Write-Up with Design Rationale
1 Certification Prep Plan
Pick A Learning Path
Pro Tip
Cloud platform specialization often improves hiring relevance because most employers screen for engineers who can immediately operate within their existing stack.
1. Data Engineering Foundations
Build the core knowledge and skills needed for a successful data engineering career.
Learn
Role clarity across core data roles
Pipelines, ETL, warehouses, and data lakes
Schemas, orchestration, and data quality
Cloud data flow fundamentals
Practice & Deliver
1 SQL Query Set on a Sample Dataset
1 Basic Python Script for Data Ingestion
1 Data Model Sketch for a Fictional Business Use Case
Pick A Learning Path
Track A
- SQL fundamentals
- Python basics
- Data warehouse orientation
Track B
- Data concepts overview
- Cloud storage basics
- Pipeline literacy
Track C
- Program orientation
- Intro to data engineering
- SQL and Python foundation
2. Core Pipeline and Modeling Skills
Build the practical pipeline and data modeling skills needed to contribute to ETL delivery, transformation logic, and data quality.
Learn
ETL patterns and batch pipeline fundamentals
Data modeling and warehouse design basics
dbt, cloud storage, and compute concepts
SQL for data transformation
Practice & Deliver
1 End-To-End Batch Pipeline Project
1 Data Model with Documented Business Logic
1 dbt Project with Tests and Documentation
Pick A Learning Path
Track A
- SQL for data engineering
- dbt basics
- Cloud warehouse setup
Track B
- Python ETL scripting
- Pipeline orchestration with Airflow
- Data quality checks
Track C
- Guided pipeline labs
- Ingestion, transformation, and loading modules
3. Cloud Platforms and Orchestration
Build the cloud platform fluency and orchestration skills needed to deploy, monitor, and operate data pipelines in production.
Learn
Cloud data platform administration basics
Airflow DAG design and scheduling patterns
Pipeline monitoring, alerting, and SLA management
Practice & Deliver
1 Airflow DAG for a Scheduled Pipeline
1 Cloud Data Warehouse Project with Documented Design
1 Pipeline Monitoring Dashboard
Pick A Learning Path
Track A
- Cloud platform deep dive
- Orchestration basics
Track B
- Airflow advanced patterns
- Pipeline monitoring and alerting
Track C
- Guided capstone project
- Mentor review
4. Projects and Portfolio
Build proof of engineering judgment by showing how you designed pipelines, handled data quality tradeoffs, made architecture decisions, and measured outcomes.
Learn
Build case studies around pipeline design decisions and architecture choices
Present options considered and tradeoffs made
Explain why you chose your approach and what you would do differently
Highlight measurable outcomes such as SLA improvement, cost reduction, or reliability gains
Practice & Deliver
End-To-End batch Pipeline Project
Streaming Ingestion Prototype
Data Model Redesign Case Study
Data Quality Framework Implementation
Cloud Cost Optimization Analysis
Pick A Learning Path
Track A
- 2 Pipeline case studies
- 1 Data model write-up
Track B
- 1 Coud architecture case study
- 1 Real-time ingestion project
- 1 Data quality framework build
Track C
- Capstone Project
- Portfolio refinement and review
5. Choose Your Specialization
Build domain fluency so your data engineering skills align more closely with the roles and industries you want to pursue.
Learn
Streaming and real-time engineering: Kafka, Flink, Kinesis, and event-driven pipeline patterns
Lakehouse and platform engineering: Delta Lake, Apache Iceberg, Databricks, and Medallion architecture
Analytics engineering: dbt advanced, Data modeling standard
ML infrastructure: Feature engineering, Data pipelines for ML, and DataOps practices
Practice & Deliver
1 Specialization-Aligned Project
1 Architecture Write-Up with Design Rationale
1 Certification Prep Plan
Pick A Learning Path
Pro Tip
Cloud platform specialization often improves hiring relevance because most employers screen for engineers who can immediately operate within their existing stack.
Key Things to Know
Start with SQL, Python, databases, ETL concepts, and basic cloud storage. Then build small pipeline projects to show practical skills.
Begin with SQL, Python, data modeling, ETL workflows, and data warehouse basics before moving into Airflow, dbt, and cloud platforms.
Build a batch data pipeline, a source-to-target mapping, a basic data model, and a data quality check script for your portfolio.
Free Data Engineer Upskilling Resources
Free Courses
Introduction to Data Analytics Course
Introduction to Data Mining Course
Basics of Data Structures and Algorithms
Become a Data Scientist: Statistics for Data Science
Introduction to Data Science with R Programming
Introduction to Applied Data Science with Python
ChatGPT for Data Analytics
Python for Data Analysis
Data Analytics Projects
SQL for Data Analysis
AWS for Data Science
Get Started with Databricks for Data Engineering
Data Analytics Course for Beginners
Free Data Analyst Course
Free Data Scientist Course
Statistics for Data Science
SQL for Data Science
Data Structures & Algorithms in Python
Introduction to Data Analytics Course
Introduction to Data Mining Course
Basics of Data Structures and Algorithms
View More
Upcoming Webinars - Free Masterclasses
Turn Raw Data into Decisions: Live Walkthrough of Creating a Tableau Dashboard
Break Into Data Analytics with this Microsoft-Backed Program
What Big Tech Like Microsoft Looks for in Software Engineers: An Insider's View
Articles and Ebooks That You Can Access For Free
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Unlocking Client Value with GenAI: A Guide for IT Service Leaders to Build Capability
How to Become Data Engineer: Skills, Jobs, & Growth Insights
GenAI in the Fast Lane - A Guide to Turbocharge Your Organization’s Capability
How to Become a Software Engineer: Roadmap and Skills
Unlocking Client Value with GenAI: A Guide for IT Service Leaders to Build Capability
How to Become Data Engineer: Skills, Jobs, & Growth Insights
GenAI in the Fast Lane - A Guide to Turbocharge Your Organization’s Capability
Connect with our learning consultant to get all your questions answered about programs, faculty, and more
Key Things to Know
Python is the most popular programming language for developing pipelines and transforming data. SQL is necessary for querying and modeling. Scala is typical of sparse or heavy-throughput streaming in Spark.
